Real-Time Avoidance of Obstacles and Emergent Geo-Fences for Urban Air Mobility Using Deep Reinforcement Learning

Urban air mobility (UAM) aims to revolutionize transportation in urban airspace by integrating advanced aerial vehicles for efficient, safe, and sustainable short-distance travel within cities. The growth in UAM operations significantly increases collision risks in dense, dynamic urban airspaces filled with buildings, obstacles, and temporary geo-fences triggered by crowded events or severe weather. Addressing the dynamic emergence of geo-fences in environments originally characterized by dense building structures presents significant challenges, and current solutions remain inadequate. In this study, we propose a novel Deep Deterministic Policy Gradient Learning Framework (DDPG), enhanced with Gated Recurrent Units (GRU), introduce a randomized layer after the neural network’s input observation and incorporate Feature Matching (FM) loss. This introduction of the randomized layer boosts the performance of autonomous aerial vehicles in avoiding dynamically emergent geo-fences in environment with dense obstacles and mitigate path deviation. Moreover the similar improvements can be found when AAV operates in unseen environments. We modeled our simulation environment using real spatial data from a residential area in Singapore, dividing it into areas for training and evaluation. Our results demonstrate that employing feature matching gated recurrent unit-deep deterministic policy gradient (FMGRU-DDPG) algorithms enables AAVs to achieve over a 90% success rate in reaching their destinations despite the presence of up to 10 dynamically appearing geo-fences during its flight. Additionally, the algorithm maintains more than a 60% success rate in unseen environments during training which outperforming both GRU-DDPG and DDPG algorithms.